Does your Ecommerce business team understand the customer journey? By tracking the history of individual customer behavior and customer interactions across different channels, your organization can better understand what motivates your audience — and cater to them with the right marketing campaigns.

Migrating data to the cloud can be a daunting task. Especially moving data from warehouses and legacy environments requires a systematic approach. These migrations usually need manual effort and can be error-prone. They are complex and involve several steps such as planning, system setup, query translation, schema analysis, data movement, validation, and performance optimization.

Here’s what you need to know about how to use your customer data platform (CDP) as your data warehouse: Whether you’re a mom-and-pop store or an ecommerce giant, understanding the customer journey is crucial to your organization’s success. When you collect data across a wide range of customer touchpoints, you can use this wealth of information for many different use cases: performing audience segmentation, improving your marketing campaigns, boosting customer engagement, and more.

This is a guest post with exclusive content by Bill Inmon. Bill Inmon is a prominent American computer scientist and prolific author, recognized by many as the father of data warehousing. Inmon has written over 60 books, including the first book exploring the core concepts of data warehouses. Inmon also held the first data warehousing conference and has written for many respected data management publications, as well as offering classes in data warehousing.

This is a guest post with exclusive content by Bill Inmon. Bill Inmon “is an American computer scientist, recognized by many as the father of the data warehouse. Inmon wrote the first book, first magazine column, held the first conference, and was the first to offer classes in data warehousing.” -Wikipedia. Our key points: One of the characteristics of most computing and analytical environments is that the environment consists of only one type of data.

This is a guest post with exclusive content by Bill Inmon. Bill Inmon “is an American computer scientist, recognized by many as the father of the data warehouse. Inmon wrote the first book, first magazine column, held the first conference, and was the first to offer classes in data warehousing.” — Wikipedia. Our critical points: Data warehouses are the whack-a-mole of technology.

Did you know that a data warehouse has the ability to completely transform your business operation’s data storage? Data warehouses serve as powerful tools in a variety of use cases in the business world. Data warehousing implementation allows companies to better process, transform, and utilize modern data. In addition, data warehouses improve how business data is stored, enhancing decision-making and business processes through more efficient data integration and data management methods.

Today’s cloud data platforms have to be simple to use and provide an intuitive user experience while not sacrificing key features and functionality.

Many businesses rely on Amazon Redshift Serverless for their cloud data warehouse and ThoughtSpot to derive insights from the data stored within. For this blog, I’m going to show you how to create a connection between Amazon Redshift Serverless and ThoughtSpot. It’s easy to connect Redshift with ThoughtSpot whether you have it running as a cluster which you have provisioned, or serverless.

Without data integration processes like ETL, today's businesses would hardly be able to make sense of the constant streams of data flowing into their tools. Of course, even though ETL is widely recognized as mission-critical to data management and business intelligence (BI) initiatives, that doesn't mean it's the most straightforward process to implement. For those looking to better understand how ETL can help their business, or what similar integration methodologies exist, this is the guide you need.